Estimation of Analog Parametric Test Metrics Using Copulas

© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.International audienceA new technique for the estimation of analog parametric test metrics at the design stage is presented in this paper. This technique employs the copulas theory to estimate the distribution between random variables that represent the performances and the test measurements of the circuit under test (CUT). A copulas-based model separates the dependencies between these random variables from their marginal distributions, providing a complete and scale-free description of dependence that is more suitable to be modeled using well-known multivariate parametric laws. The model can be readily used for the generation of an arbitrarily large sample of CUT instances. This sample is thereafter used for estimating parametric test metrics such as defect level (or test escapes) and yield loss. We demonstrate the usefulness of the proposed technique to evaluate a built-in-test technique for a radio frequency low noise amplifier and to set test limits that result in a desired tradeoff between test metrics. In addition, we compare the proposed technique with previous ones that rely on direct density estimation

SymBIST: Symmetry-based Analog/Mixed-Signal BIST

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Symmetry-based A/M-S BIST (SymBIST): Demonstration on a SAR ADC IP

International audienceIn this paper, we propose a defect-oriented Built-In Self-Test (BIST) paradigm for analog and mixed-signal (A/M-S) Integrated Circuits (ICs), called symmetry-based BIST (Sym-BIST). SymBIST exploits inherent symmetries into the design to generate invariances that should hold true only in defect-free operation. Violation of any of these invariances points to defect detection. We demonstrate SymBIST on a 65nm 10-bit Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) IP by ST Microelectronics

An analog checker with inputrelative tolerance for duplicate signals

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Self-Testing Analog Spiking Neuron Circuit

International audienceHardware-implemented neural networks are foreseen to play an increasing role in numerous applications. In this paper, we address the problem of post-manufacturing test and self-test of hardware-implemented neural networks. In particular, we propose a self-testable version of a spiking neuron circuit. The self-test wrapper is a compact circuit composed of a low-precision ramp generator and a small digital block. The self-test principle is demonstrated on a spiking neuron circuit design in 0.35µm CMOS technology

Compact Functional Testing for Neuromorphic Computing Circuits

We address the problem of testing artificial intelligence (AI) hardware accelerators implementing spiking neural networks (SNNs). We define a metric to quickly rank available samples for training and testing based on their fault detection capability. The metric measures the interclass spike count difference of a sample for the fault-free design. In particular, each sample is assigned a score equal to the spike count difference between the first two top classes. The hypothesis is that samples with small scores achieve high fault coverage because they are prone to misclassification, i.e., a small perturbation in the network due to a fault will result in these samples being misclassified with high probability. We show that the proposed metric correlates with the per-sample fault coverage and that retaining a set of high-ranked samples in the order of ten achieves near-perfect fault coverage for critical faults that affect the SNN accuracy. The proposed test generation approach is demonstrated on two SNNs modeled in Python and on actual neuromorphic hardware. We discuss fault modeling and perform an analysis to reduce the fault space so as to speed up test generation time. © 1982-2012 IEEE

Securing Programmable Analog ICs Against Piracy

International audienceIn this paper, we demonstrate a security approach for the class of highly-programmable analog Integrated Circuits (ICs) that can be used as a countermeasure for unauthorized chip use and piracy. The approach relies on functionality locking, i.e. a lock mechanism is introduced into the design such that unless the correct key is provided the functionality breaks. We show that for highly-programmable analog ICs the programmable fabric can naturally be used as the lock mechanism. We demonstrate the approach on a multi-standard RF receiver with configuration settings of 64-bit words

Enrichment of limited training sets in machine-learning-based analog/RF Test

Abstract-This paper discusses the generation of informationrich, arbitrarily-large synthetic data sets which can be used to (a) efficiently learn tests that correlate a set of low-cost measurements to a set of device performances and (b) grade such tests with parts per million (PPM) accuracy. This is achieved by sampling a non-parametric estimate of the joint probability density function of measurements and performances. Our case study is an ultra-high frequency receiver front-end and the focus of the paper is to learn the mapping between a lowcost test measurement pattern and a single pass/fail test decision which reflects compliance to all performances. The small fraction of devices for which such a test decision is prone to error are identified and retested through standard specification-based test. The mapping can be set to explore thoroughly the tradeoff between test escapes, yield loss, and percentage of retested devices

FTO influences adipogenesis by regulating mitotic clonal expansion

The fat mass and obesity-associated (FTO) gene plays a pivotal role in regulating body weight and fat mass; however, the underlying mechanisms are poorly understood. Here we show that primary adipocytes and mouse embryonic fibroblasts (MEFs) derived from FTO overexpression (FTO-4) mice exhibit increased potential for adipogenic differentiation, while MEFs derived from FTO knockout (FTO-KO) mice show reduced adipogenesis. As predicted from these findings, fat pads from FTO-4 mice fed a high-fat diet show more numerous adipocytes. FTO influences adipogenesis by regulating events early in adipogenesis, during the process of mitotic clonal expansion. The effect of FTO on adipogenesis appears to be mediated via enhanced expression of the pro-adipogenic short isoform of RUNX1T1, which enhanced adipocyte proliferation, and is increased in FTO-4 MEFs and reduced in FTO-KO MEFs. Our findings provide novel mechanistic insight into how upregulation of FTO leads to obesity

A Systematic Mapping Approach of 16q12.2/FTO and BMI in More Than 20,000 African Americans Narrows in on the Underlying Functional Variation: Results from the Population Architecture using Genomics and Epidemiology (PAGE) Study

Genetic variants in intron 1 of the fat mass- and obesity-associated (FTO) gene have been consistently associated with body mass index (BMI) in Europeans. However, follow-up studies in African Americans (AA) have shown no support for some of the most consistently BMI-associated FTO index single nucleotide polymorphisms (SNPs). This is most likely explained by different race-specific linkage disequilibrium (LD) patterns and lower correlation overall in AA, which provides the opportunity to fine-map this region and narrow in on the functional variant. To comprehensively explore the 16q12.2/FTO locus and to search for second independent signals in the broader region, we fine-mapped a 646-kb region, encompassing the large FTO gene and the flanking gene RPGRIP1L by investigating a total of 3,756 variants (1,529 genotyped and 2,227 imputed variants) in 20,488 AAs across five studies. We observed associations between BMI and variants in the known FTO intron 1 locus: the SNP with the most significant p-value, rs56137030 (8.3×10-6) had not been highlighted in previous studies. While rs56137030was correlated at r2>0.5 with 103 SNPs in Europeans (including the GWAS index SNPs), this number was reduced to 28 SNPs in AA. Among rs56137030 and the 28 correlated SNPs, six were located within candidate intronic regulatory elements, including rs1421085, for which we predicted allele-specific binding affinity for the transcription factor CUX1, which has recently been implicated in the regulation of FTO. We did not find strong evidence for a second independent signal in the broader region. In summary, this large fine-mapping study in AA has substantially reduced the number of common alleles that are likely to be functional candidates of the known FTO locus. Importantly our study demonstrated that comprehensive fine-mapping in AA provides a powerful approach to narrow in on the functional candidate(s) underlying the initial GWAS findings in European populations